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Multi-class Bitcoin mixing service identification based on graph classification

Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training 41

Improving Accuracy-robustness Trade-off via Pixel Reweighted...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Jiacheng Liu, Feng Zhou, Dawei Zhang, Jingfeng Liu, Tongliang School of Computing and Information Systems The University of Melbourne Australia State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science The University of Auckland New Zealand RIKEN AIP Japan Sydney AI Centre The University of Sydney Australia

Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the *** perturbations are constrained by a predefined perturbation budget ϵ and are equally applied to each pixel within an ***, in this paper, we discover that not all pixels contribute equally to the accuracy on AEs (i.e., robustness) and accuracy on natural images (i.e., accuracy).Motivated by this finding, we propose Pixel-reweighted AdveRsarial Training (PART), a new framework that partially reduces ϵ for less influential pixels, guiding the model to focus more on key regions that affect its ***, we first use class activation mapping (CAM) methods to identify important pixel regions, then we keep the perturbation budget for these regions while lowering it for the remaining regions when generating *** the end, we use these pixel-reweighted AEs to train a *** achieves a notable improvement in accuracy without compromising robustness on CIFAR-10, SVHN and TinyImagenet-200, justifying the necessity to allocate distinct weights to different pixel regions in robust classification. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learning

arXiv

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arXiv 2024年

作者： Gan, Kai Wei, Tong Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in many real-world classification problems, existing LTSSL algorithms typically assume that the class distributions of labeled and unlabeled data are almost identical. Those LTSSL algorithms built upon the assumption can severely suffer when the class distributions of labeled and unlabeled data are mismatched since they utilize biased pseudo-labels from the model. To alleviate this problem, we propose a new simple method that can effectively utilize unlabeled data from unknown class distributions through Boosting cOnsistency in duAl Training (BOAT). Specifically, we construct the standard and balanced branch to ensure the performance of the head and tail classes, respectively. Throughout the training process, the two branches incrementally converge and interact with each other, eventually resulting in commendable performance across all classes. Despite its simplicity, we show that BOAT achieves state-of-the-art performance on a variety of standard LTSSL benchmarks, e.g., an averaged 2.7% absolute increase in test accuracy against existing algorithms when the class distributions of labeled and unlabeled data are mismatched. Even when the class distributions are identical, BOAT consistently outperforms many sophisticated LTSSL algorithms. We carry out extensive ablation studies to tease apart the factors that are the most important to the success of BOAT. The source code is available at https://***/Gank0078/BOAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Multimodal Aspect-Based Sentiment Analysis under Conditional Relation 31

Multimodal Aspect-Based Sentiment Analysis under Conditional...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinjing Li, Ruifan Ye, Shuqin Zhang, Guangwei Wang, Xiaojie School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Computer Science Beijing University of Posts and Telecommunications China Engineering Research Center of Information Networks Ministry of Education China Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism China

ISBN: (纸本)9798891761964

Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms from text-image pairs and identify their sentiments. Previous methods are based on the premise that the image contains the objects referred by the aspects within the text. However, this condition cannot always be met, resulting in a suboptimal performance. In this paper, we propose COnditional Relation based Sentiment Analysis framework (CORSA). Specifically, we design a conditional relation detector (CRD) to mitigate the impact of the unmet conditional image. Moreover, we design a visual object localizer (VOL) to locate the exact condition-related visual regions associated with the aspects. With CRD and VOL, our CORSA framework takes a multi-task form. In addition, to effectively learn CORSA we conduct two types of annotations. One is the conditional relation using a pretrained referring expression comprehension model;the other is the bounding boxes of visual objects by a pretrained object detection model. Experiments on our built C-MABSA dataset show that CORSA consistently outperforms existing methods. The code and data are available at https://***/Liuxj-Anya/CORSA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Nanoseconds CDR Enabled by Clock Distribution and Protection Sequence Insertion in Optical

Nanoseconds CDR Enabled by Clock Distribution and Protection...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Zhao, Yisong Yang, Changsheng Dang, Daohang Guo, Yuanzhi Shen, Shikui Wen, Ran Chen, Bin Guo, Bingli Yang, Chuanchuan Hua, Nan Xue, Xuwei Huang, Shanguo Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China China Unicom Research Institute Beijing China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Peking University The State Key Laboratory of Advanced Optical Communication Systems and Networks Beijing China Beijing China

The scheme combining clock distribution and protection sequence insertion from the network perspective is proposed and experimentally investigated to implement a 3.1 nanoseconds clock and data recovery in optical swit... 详细信息

ISBN: (纸本)9781665462136

关键词： Optical switches

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Spatial-Temporal Aware Business Event Forecasting for Proactive Services from IoT Sensory Data 26

Spatial-Temporal Aware Business Event Forecasting for Proact...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Jiayu Wang, Guiling Shi, Yongpeng Zheng, Xin Yu, Jian Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand

ISBN: (纸本)9798350331684

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios. © 2023 IEEE.

关键词： Forecasting

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H-MGSR: A Hierarchical Motif-based Graph Attention Neural Network for Service Recommendation

H-MGSR: A Hierarchical Motif-based Graph Attention Neural Ne...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zheng, Xin Wang, Guiling Zhang, Jiayu Zhang, Yuqi Wang, Nancy Yu, Jian Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand

ISBN: (纸本)9798350304855

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain. © 2023 IEEE.

关键词： Graph neural networks

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A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM 26

A Dynamic Binding Method for Situation-aware IoT Services Ta...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Li, Xiuxian Wang, Guiling Shi, Yongpeng Yu, Jian Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand

ISBN: (纸本)9798350331684

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method. © 2023 IEEE.

关键词： Dynamics

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Cryptanalysis of PRIMATEs

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Science China(information Sciences) 2020年第1期63卷 130-139页

作者： Yanbin LI Meiqin WANG Wenqing LIU Wei WANG Key Laboratory of Cryptologic Technology and Information Security Ministry of EducationShandong University Shandong Provincial Key Laboratory of Computer Networks

PRIMATEs is a family of authenticated encryption design submitted to competition for authenticated encryption: security, applicability, and robustness. The three modes of operation in PRIMATEs family are: APE, HANUMAN, GIBBON with security levels: 80, 120 bits. APE is robust despite the nonce misusing. In this study, we revise the algebraic model and find new integral distinguishers for both PRIMATE permutation and its inverse permutation. Moreover, we construct a zero-sum distinguisher for full12-round PRIMATE-80/120 permutation with the 2;/2;complexity, improving over previous work. We also perform an integral attack on 8-round finalization of APE-80/120 with 2;chosen messages. The key recovery process is optimized using the FFT technique presented by Todo and Aoki. Our work is the best attack against APE, demonstrating the practical attack on 8-round finalization of APE-80. The new integral distinguishers apply to create forgeries on 5/6-round finalization of APE and HANUMAN that require2;/2;chosen messages, which is the first forgery attack against APE and HANUMAN.

关键词： PRIMATEs APE HANUMAN integral distinguisher key recovery attack forgery

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Integrated Communication, Navigation, and Remote Sensing in LEO networks with Vehicular Applications

arXiv

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arXiv 2024年

作者： Sheng, Min Guo, Chongtao Huang, Lei State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The State Key Laboratory of Radio Frequency Heterogeneous Integration College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Traditionally, communication, navigation, and remote sensing (CNR) satellites are separately performed, leading to resource waste, information isolation, and independent optimization for each functionality. Taking future automated driving as an example, it faces great challenges in providing high-reliable and low-latency lane-level positioning, decimeter-level transportation observation, and huge traffic sensing information downloading. To this end, this article proposes an integrated CNR (ICNR) framework based on low Earth orbit (LEO) satellite mega-constellations. After introducing the main working principles of the CNR functionalities to serve as the technological basis, we characterize the potentials of the integration gain in vehicular use cases. Then, we investigate the ICNR framework in different integration levels, which sheds strong light on qualitative performance improvement by sophisticatedly sharing orbit constellation, wireless resource, and data information towards meeting the requirements of vehicular applications. We also instantiate a fundamental numerical case study to demonstrate the integration gain and highlight possible future research directions in managing the ICNR networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite communication systems

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